Analytics of privacy often focus on issues of data confidentiality. However, additionally important to individual privacy can be inferences that can be drawn about each of us, for example, by predictive models that can be based on seemingly benign data. Recent studies have explored the predictive power of information disclosed on social networks, such as Facebook® (“Facebook”), to infer users' personal characteristics. In particular, it has been shown that analyzing the pages that a user “Likes” on Facebook can be used to predict characteristics, such as their personality traits, their age or their sexual orientation. These results can be surprising, and can be viewed as privacy intrusions to users who do not wish to reveal their personal characteristics.
Privacy in social media is becoming an increasing concern for consumers, regulators and policy makers. A recent study based on a survey of Facebook users found that users did not feel that they had the appropriate tools to mitigate their privacy concerns within their social network connections. One source of concern can be the extent of the inferences which can be drawn using social media. Previous work has explored the revealing nature of disclosing personal taste-oriented information in online social networks. Various personal characteristics, including intelligence, introversion/extroversion, sexual orientation, and the like, can be predicted with surprising accuracy based on the “Likes” one reveals on Facebook. Facebook has tied the previously free-response based favorite movies, television shows, and music sections of a user's profile to these “Likes”, creating quantifiable connections from users to additional pages. These “Liked” pages can be utilized to predict personal characteristics that a user may not want to disclose on their Facebook profile.
To many, privacy invasions via inference by predictive models can be at least as troublesome as privacy invasions based directly on primary data. There is some evidence that when given the appropriate tools, people may trade off the benefit of their online activity with their privacy concerns.
Various pricing strategies, marketing campaigns and political campaigns can rely on the ability to correctly estimate potential customers' or voters' preferences. This can generate incentives for firms and governments to acquire information related to people's personal characteristics, such as their gender, marital status, religion, sexual or political orientation. The boom in availability of online data has accentuated their efforts to do so. However, personal characteristics often can be difficult to determine directly, and with certainty, because of privacy restrictions. Thus, marketers increasingly rely on statistical inferences based on available information. A predictive model can be used to give each user a score that can be proportional to the probability of having a certain personal trait, such as being gullible, introverted, female, a drug user, gay, etc. (See, e.g., Reference 8) Users can then be targeted based on their predicted propensities and the relationships of these inferences to the particular campaign. Alternatively, such characteristics can be used implicitly in campaigns, based on feedback from those who responded positively. In practice, a combination of model confidence and a budget for showing ads can lead campaigns to target users in some top percentile of the score distribution given by the predictive models. (See, e.g., Reference 13).
Traditionally, online user targeting systems have been trained using information on users' web browsing behavior. (See, e.g., Reference 14). However, a growing trend can be to include information disclosed by users on social networks. (See, e.g., Reference 1). For example, Facebook has recently deployed a system that can facilitate third party applications to display advertisements on their platform using their users' profile information, such as the things they explicitly indicate that they “Like.” (See, e.g., Reference 2).
While some online users can benefit from being targeted based on inferences of their personal characteristics, others can find such inferences unsettling. Not only can these inferences be incorrect due to a lack of data or inadequate models, some users may not wish to have certain characteristics inferred about them at all. In response to an increase in demand for privacy from online users, suppliers of browsers such as Chrome and Firefox have developed features such as “Do Not Track,” “Incognito,” and “Private Windows” to control the collection of information about web browsing. However, as of now, social networks such as Facebook do not have a strong analog to these features which can facilitate transparency and control over how user information can be used to decide on the presentation of content and advertisements.
Thus, it may be beneficial to provide an exemplary system, method and computer-accessible medium that can facilitate the ability for a user to regulate their privacy on the internet by examining inferences that can be made based on a user's “Likes”, as well as to address at least some of the deficiencies described herein above.